Improving land change detection based on uncertain survey maps using fuzzy sets

In this paper we present a method for correcting inherent classification bias in historical survey maps with which subsequent land cover change analysis can be improved. We linked generalized linear modelling techniques for spatial uncertainty prediction to fuzzy set based operations. The predicted uncertainty information was used to compute fuzzy memberships of forest and non-forest classes at each location. These memberships were used to reclassify the original map based on decision rules, which take into consideration the differences in identification likelihood during the historical mapping. Since the forest area was underestimated in the original mapping, the process allows to correct this bias by favouring forest, especially where uncertainty was high. The analyses were performed in a cross-wise manner between two study areas in order to examine whether the bias correction algorithm would still hold in an independent test area. Our approach resulted in a significant improvement of the original map as indicated by an increase of the Normalized Mutual Information from 0.26 and 0.36 to 0.38 and 0.45 for the cross-wise test against reference maps in Pontresina and St. Moritz, respectively. Consequently subsequent land cover change assessments could be considerably improved by reducing the deviations from a reference change by almost 50 percent. We concluded that the use of logistic regression techniques for uncertainty modelling based on topographic gradients and fuzzy set operations are useful tools for predictively reducing uncertainty in maps and land cover change models. The procedure allows to get more reliable area estimates of crisp classes and it improves the computation of the fuzzy areas of classes. The approach has limitations when the original map shows high initial accurac

Natural forest regrowth as a proxy variable for agricultural land abandonment in the Swiss mountains: a spatial statistical model based on geophysical and socio-economic variables

In many European mountain regions, natural forest regrowth on abandoned agricultural land and the related consequences for the environment are issues of increasing concern. We developed a spatial statistical model based on multiple geophysical and socio-economic variables to investigate the pattern of natural forest regrowth in the Swiss mountain area between the 1980s and 1990s. Results show that forest regrowth occurred primarily in areas with low temperature sum, intermediate steepness and soil stoniness as well as close to forest edges and relatively close to roads. Model results suggest that regions with weak labor markets are favored in terms of land abandonment and forest regrowth. We could not find an effect of population change on land abandonment and forest regrowth. Therefore, we conclude that decision makers should consider non-linearities in the pattern of forest regrowth and the fact that labor markets have an effect on land abandonment and forest regrowth when designing measures to prevent agricultural land abandonment and natural forest regrowth in the Swiss mountain

The upward shift in altitude of pine mistletoe ( Viscum album ssp. austriacum ) in Switzerland—the result of climate warming?

Pine mistletoe (Viscum album ssp. austriacum) is common in natural Scots pine (Pinus sylvestris L.) forests in the alpine Rhone Valley, Switzerland. This semi-parasite, which is regarded as an indicator species for temperature, increases the drought stress on trees and may contribute to the observed pine decline in the region. We recorded mistletoes on representative plots of the Swiss National Forest Inventory ranging from 450 to 1,550m a.s.l. We found mistletoe on 37% of the trees and on 56% of all plots. Trees infested with mistletoe had a significantly higher mortality rate than non-infested trees. We compared the current mistletoe occurrence with records from a survey in 1910. The current upper limit, 1,250m, is roughly 200m above the limit of 1,000-1,100m found in the earlier survey 100 years ago. Applying a spatial model to meteorological data we obtained monthly mean temperatures for all sites. In a logistic regression mean winter temperature, pine proportion and geographic exposition significantly explained mistletoe occurrence. Using mean monthly January and July temperatures for 1961-1990, we calculated Skre's plant respiration equivalent (RE) and regressed it against elevation to obtain the RE value at the current mistletoe elevation limit. We used this RE value and temperature from 1870-1899 in the regression and found the past elevation limit to be at 1,060m, agreeing with the 1910 survey. For the predicted temperature rise by 2030, the limit for mistletoe would increase above 1,600m altitud

Global climate-related predictors at kilometer resolution for the past and future

A multitude of physical and biological processes on which ecosystems and human societies depend are governed by the climate, and understanding how these processes are altered by climate change is central to mitigation efforts. We developed a set of climate-related variables at as yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We downscaled time series of near-surface relative humidity (hurs) and cloud area fraction (clt) under the consideration of orography and wind as well as near-surface wind speed (sfcWind) using the delta-change method. Combining these grids with mechanistically downscaled information on temperature, precipitation, and solar radiation, we then calculated vapor pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), the climate moisture index (cmi), and site water balance (swb) at a monthly temporal and 30 arcsec spatial resolution globally from 1980 until 2018 (time-series variables). At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981–2010, 2011–2040, 2041–2070, and 2071–2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models (projected variables). Time-series variables showed high accuracy when validated against observations from meteorological stations and when compared to alternative products. Projected variables were also highly correlated with observations, although some variables showed notable biases, e.g., snow cover days. Together, the CHELSA-BIOCLIM+ dataset presented here (https://doi.org/10.16904/envidat.332, Brun et al., 2022) allows improvement to our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world's ecosystems and the associated services on societies.</p

Enhanced Response of Global Wetland Methane Emissions to the 2015-2016 El Nino-Southern Oscillation Events

Wetlands are thought to be the major contributor to interannual variability in the growth rate of atmospheric methane (CH4) with anomalies driven by the influence of the El Nio-Southern Oscillation (ENSO). Yet it remains unclear whether (i) the increase in total global CH4 emissions during El Nino versus La Nina events is from wetlands and (ii) how large the contribution of wetland CH4 emissions is to the interannual variability of atmospheric CH4. We used a terrestrial ecosystem model that includes permafrost and wetland dynamics to estimate CH4 emissions, forced by three separate meteorological reanalyses and one gridded observational climate dataset, to simulate the spatio-temporal dynamics of wetland CH4 emissions from 1980-2016. The simulations show that while wetland CH4 responds with negative annual anomalies during the El Nino events, the instantaneous growth rate of wetland CH4 emissions exhibits complex phase dynamics. We find that wetland CH4 instantaneous growth rates were declined at the onset of the 2015-2016 El Nino event but then increased to a record-high at later stages of the El Nino event (January through May 2016). We also find evidence for a step increase of CH4 emissions by 7.8+/-1.6 Tg CH4 per yr during 2007-2014 compared to the average of 2000-2006 from simulations using meteorological reanalyses, which is equivalent to a approx.3.5 ppb per yr rise in CH4 concentrations. The step increase is mainly caused by the expansion of wetland area in the tropics (30 deg S-30 deg N) due to an enhancement of tropical precipitation as indicated by the suite of the meteorological reanalyses. Our study highlights the role of wetlands, and the complex temporal phasing with ENSO, in driving the variability and trends of atmospheric CH4 concentrations. In addition, the need to account for uncertainty in meteorological forcings is highlighted in addressing the interannual variability and decadal-scale trends of wetland CH4 fluxes

Enhanced Response of Global Wetland Methane Emissions to Recent El Nino-Southern Oscillation Events

Wetlands are thought to be the major contributor to interannual variability in the growth rate of atmospheric methane (CH4) with anomalies driven by the influence of the El Nino-Southern Oscillation (ENSO). Yet it remains unclear whether (i) the increase in total global CH4 emissions during El Nino versus La Ni na events is from wetlands and (ii) how large the contribution of wetland CH4 emissions is to the interannual variability of atmospheric CH4. We used a terrestrial ecosystem model that includes permafrost and wetland dynamics to estimate CH4 emissions, forced by three separate meteorological reanalyses and one gridded observational climate dataset, to simulate the spatio-temporal dynamics of wetland CH4 emissions from 1980-2016. The simulations show that while wetland CH4 responds with negative annual anomalies during the El Nino events, the instantaneous growth rate of wetland CH4 emissions exhibits complex phase dynamics. We find that wetland CH4 instantaneous growth rates were declined at the onset of the 2015-2016 El Nino event but then increased to a record-high at later stages of the El Nino event (January through May 2016). We also find evidence for a step increase of CH4 emissions by 7.8+/-1.6 Tg CH4 yr1 during 2007-2014 compared to the average of 2000-2006 from simulations using meteorological reanalyses, which is equivalent to a 3.5 ppb yr1 rise in CH4 concentrations. The step increase is mainly caused by the expansion of wetland area in the tropics (30S-30N) due to an enhancement of tropical precipitation as indicated by the suite of the meteorological reanalyses. Our study highlights the role of wetlands, and the complex temporal phasing with ENSO, in driving the variability and trends of atmospheric CH4 concentrations. In addition, the need to account for uncertainty in meteorological forcings is highlighted in addressing the interannual variability and decadal-scale trends of wetland CH4 fluxes